Zenity sees the app surface.
Trust3 AI sees the data layer.
Both platforms exist to govern AI agents. They enforce at different layers, and that decides which one fits your environment. Zenity is built for agents running on managed SaaS platforms. Trust3 AI is built for enterprises that need one policy enforced across custom-built agents, multiple clouds, and the data platforms those agents reach.
Which platform fits your stack
Zenity
- Your agent footprint sits almost entirely inside vendor-managed SaaS platforms
- The mandate is discovery and posture management inside those platforms
Trust3 AI
- Agents are built on more than one framework, or your teams build their own
- Agents run across more than one cloud
- Policy has to be enforced before an action, not reported after it
- Multi-agent workflows hand work between agents and the originating identity has to survive the chain
- Agents query Snowflake, Databricks, BigQuery, or Iceberg and access has to be controlled at the source
- MCP servers and tool calls need fine-grained access control
- Auditors want enforcement evidence, not observation logs
Where Trust3 AI goes further than Zenity
Zenity has real depth across managed SaaS agent runtimes — Microsoft 365 Copilot, Power Platform, and Salesforce Agentforce — with solid discovery, posture management, and shadow AI detection inside those environments. Here is where the architectures diverge.
Every framework and cloud, not just managed SaaS
Zenity is strongest where a SaaS vendor controls the agent runtime, so coverage follows the platform. Trust3 AI enforces regardless of where the agent runs or what built it — LangChain, Amazon Bedrock, Azure AI Foundry, Copilot Studio, Databricks, and agents your teams built in-house all sit under one control plane, with no per-framework shims to maintain.
Enforcement before the action, not after
Zenity’s architecture is monitoring-first: visibility and posture are strong, but inline enforcement before tool invocation is not its primary design pattern. Trust3 AI enforces at the moment of action, before data moves or a tool executes. Policy fires first, then the action happens or it does not.
Identity survives multi-hop delegation
Agents delegate — one agent calls another, that one calls a tool, the tool queries a warehouse. By the third hop the originating user’s identity is usually gone. Trust3 AI carries agent identity, declared purpose, and live policy state across every hop; scope can only narrow, never expand, and the full chain is recorded as one correlated audit record.
Declared purpose as a first-class enforcement primitive
Role-based access asks who the agent is. Trust3 AI also asks what the agent was declared to do — an agent declared as a customer support tool cannot reach payroll data even when its credentials would permit it. Grants are just-in-time and expire with the task.
Native enforcement at the data layer
Row, column, and tag-based policy enforced natively at the source across Snowflake, Databricks, BigQuery, and Apache Iceberg, with no proxy hop.
Side-by-side capability comparison
| Capability | Trust3 AI | Zenity |
|---|---|---|
| Agent discovery and observability | Auto-discovers every agent across any framework, cloud, or custom build, including shadow AI and ephemeral identities. End-to-end traces of every agent step, input to output. | Strong discovery within managed SaaS agent environments. |
| Cross-framework coverage | LangChain, Amazon Bedrock, Azure AI Foundry, Copilot Studio, Databricks, and custom agents, plus 50+ platforms | Strongest where a SaaS vendor controls the agent runtime |
| MCP security | Enforces at the Model Context Protocol layer. Every MCP server is untrusted by default. | Not a documented primary capability |
| A2A security | Identity and policy enforcement maintained through agent-to-agent delegation chains | Not a documented primary capability |
| Data access enforcement | Row, column, and tag-based policy enforced natively at the source across Snowflake, Databricks, BigQuery, and Apache Iceberg, with no proxy hop | Not a documented primary capability |
| Enforcement timing | Inline, before data moves or a tool fires | Monitoring-first, with posture management |
| Multi-hop policy propagation | AI-native metadata carries agent identity, declared purpose, and policy state across every hop | Not a documented primary capability |
| Declared purpose enforcement | Core policy primitive, evaluated per request | Not a documented primary capability |
| Cloud coverage | AWS, Microsoft Azure, Google Cloud | Strongest inside vendor-managed agent platforms |
What this looks like in production
The production governance gap
48% of AI agents in production are running unsecured, according to Gravitee’s The State of AI Agent Security 2026, a survey of 750 senior technology leaders published June 2026.
Monitoring alone does not close that gap. Seeing an unauthorized data access after it happened is evidence, not control. The control plane has to sit inline, and the policy has to fire before the action.
Frequently asked questions
Which platform is better for cross-framework agent governance?
Trust3 AI, when agents run on more than one framework or cloud. Enforcement does not depend on a single vendor controlling the agent runtime. Zenity is the stronger fit when the priority is discovery and posture management fully inside vendor-managed SaaS agent platforms like Microsoft 365 Copilot, Power Platform, and Salesforce Agentforce.
Does Trust3 AI support MCP and A2A security?
Yes to both. Every MCP server is treated as untrusted by default, with server verification, credential isolation through short-lived task-scoped tokens, and identity propagation through the MCP call to the data source. For A2A, identity and declared purpose propagate through every hop, scope can only narrow, and the full chain is recorded as one audit record tied to the originating user.
What is declared purpose enforcement?
Access is granted against what an agent was declared to do, not against a role it inherited. A support agent scoped to ticket data cannot reach payroll data even if its credentials would allow it. Grants expire when the task ends.
How does Trust3 AI handle multi-agent workflows?
Agent identity, declared purpose, and live policy state travel with every request across every hop. Policy is evaluated at each hop with the full chain in context.
We already govern access in Snowflake and Databricks. Why add Trust3 AI?
Those platforms secure the payload. By the time an agent chain reaches the platform, the originating user identity is usually gone and a service account is what the data layer sees. Policy cannot fire against a principal that was lost three hops ago. Trust3 AI compiles policy into native enforcement in each platform and carries the real identity to it.
Is either platform self-serve?
Both are enterprise sales. Neither publishes pricing or offers a self-serve trial.
Does Trust3 AI help with regulatory compliance?
Trust3 AI ships pre-built compliance packs covering GDPR, HIPAA, SOX, NIST AI RMF, EU AI Act, CCPA, and PCI-DSS, with one-click evidence generation that updates as standards evolve. The evidence produced is enforcement evidence, not observation logs.
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